基于LSTM和CatBoost组合模型的短期负荷预测

Short-term Load Forecasting Based on LSTM and CatBoost Combined Model

针对现有的电力系统短期负荷预测方法存在预测精度较差的问题,提出一种基于长短期记忆神经网络(Long short term memory,LSTM)和CatBoost组合的短期负荷预测方法,针对电力负荷数据具有时序性和非线性的特点,以及长短期记忆网络不能直接处理类别型特征,对处理后的电力负荷数据建立LSTM负荷预测模型和CatBoost负荷预测模型;用方差倒数法确定加权系数,得到LSTM和CatBoost组合模型的预测值;最后使用实际负荷数据对算法有效性进行验证,预测结果表明采用LSTM和CatBoost组合模型的方法在负荷预测精度上有显著的提高。

In view of the poor prediction accuracy of existing short-term load forecasting methods for power systems,a short-term load forecasting method based on the combination of long short term memory(LSTM)and CatBoost is proposed.Firstly,in view of the time-series and nonlinear characteristics of the power load data,and the fact that the long-term and short-term memory networks can not directly deal with the categorical features,the LSTM load forecasting model and the CatBoost load forecasting model are established for the processed power load data.Secondly,the weighted coefficients are determined by the inverse variance method,and the predicted values of the LSTM and CatBoost models are obtained.Finally,the validity of the algorithm is verified by the actual load data.The prediction results show that the combined model of LSTM and CatBoost can significantly improve the accuracy of load forecasting.